Association between dry eye disease and temperature is modified by residential greenness: case-crossover study in Beijing, China

Shuting Liang; Chenghua Guo; Jingyi Wu; Pengfei Li

doi:10.1186/s12889-025-25885-1

. 2025 Dec 9;26:177. doi: 10.1186/s12889-025-25885-1

Association between dry eye disease and temperature is modified by residential greenness: case-crossover study in Beijing, China

Shuting Liang ^1,^3,^#, Chenghua Guo ^2,^4,^#, Jingyi Wu ^4,^✉, Pengfei Li ^2,^4,^5,^✉

PMCID: PMC12802197 PMID: 41366347

Abstract

Background

Dry eye disease (DED) is one of the most prevalent eye conditions globally. While the harmful effects of air pollution on DED are well-documented, the role of temperature—an important factor linked to climate change—remains less understood. Green spaces have been identified as a potential way to reduce health risks from climate change, yet few studies have examined their combined influence on DED.

Methods

A time-stratified case-crossover study was conducted using data of 71,069 first-time outpatient visits for DED at Peking University People’s Hospital in Beijing, from January 2012, to October 2022. We analyzed the linear association between daily average temperature and DED using a conditional logistic regression model and developed a non-linear exposure-response function linking DED to temperature exposure. Participants’ normalized difference vegetation index (NDVI) was estimated and used to represent greenness exposure, and a novel varying-coefficient model was used to explore the non-linear modifying effects of greenness exposure on the temperature-DED association.

Results

Our findings indicate a positive association between temperature and DED, with a 0.8% excess risk (95% confidence interval [CI] 0.4%, 1.2%) for per 1 °C increment in temperature. Residential greenness appeared to have a protective effect against temperature-related DED. Participants living in areas with the lowest NDVI were the most vulnerable, experiencing a 1.8% excess risk (95% CI 1.1%, 2.5%) of temperature-related DED, while no significant temperature effect was observed in areas with high NDVI. The varying-coefficient model suggests a sublinear curve for the protective modifying effect of greenness on the population vulnerability to temperature.

Conclusion

High temperatures were linked to an increased risk of DED, while residential greenness provided a protective buffer against temperature-related risks. These results highlight the need for urban planning and public health strategies that incorporate green spaces to help mitigate the growing burden of DED in the context of climate change.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-025-25885-1.

Keywords: Dry eye disease, Temperature, Greenness, Climate change, Public-health policy

Introduction

Dry eye disease (DED), a multifactorial disorder characterized by a loss of tear film homeostasis, is one of the most common ocular diseases in the world. The global prevalence of DED is reported to range from 5% to 50%, reflecting substantial variability in study design, diagnostic criteria, and population characteristics [1]. Studies based primarily on symptoms tend to report lower prevalence, while those using clinical signs, such as measures of tear quality, tear stability, or lid abnormalities, often yield higher rates [1]. In China, a meta-analysis found that 31.4% of individuals experienced DED, affecting nearly 394 million people [2]. In addition to the physical discomfort, such as dryness, itching, and burning, and the diminished vision-related quality of life, DED also imposes substantial socioeconomic burdens, including both direct medical costs and indirect costs such as reduced workplace productivity [3, 4].

Seasonal variation in DED occurrence has been observed, with researches reporting the lowest incidence in winter compared to the other three seasons [5, 6]. These seasonal patterns may be influenced by environmental factors like temperature and humidity, which can trigger DED symptoms [7]. In 2023, China’s national average temperature was 0.82 °C higher than the 1981–2020 annual average, with high-temperature days (daily maximum temperature ≥ 35.0 °C) increasing by 4.4 days above the 1981–2020 average [8]. Despite these rising temperatures, research on the specific effects of temperature on DED risk remains limited. Single-center studies conducted in Taiwan [6] and South Korea [9] have reported significant positive associations between temperature and DED. However, these findings may not be generalizable due to regional differences in climate, socioeconomic, and demographic factors. Further research on temperature and DED in diverse geographic settings was needed.

Greenness has emerged as a potential climate change mitigation strategy, offering micro-climate regulation and air filtration [10, 11]. Greenness has been reported to mitigate high-temperature-related mortality in elderly populations [12–15]. In shaping the local thermal environment, greenness plays an important role by providing shade, absorbing thermal radiation through transpiration, and sucking up greenhouse gas [16]. In urban areas affected by the heat island effect, greenness can effectively reduce air temperatures by 0.5 to 4.0 °C through evapotranspiration [17]. Previous research has shown that exposure to greenness is associated with various ophthalmic conditions, including keratoconus [18] and myopia [19, 20]. Recently, a case–control study in China reported that higher long-term green space exposure was significantly associated with lower odds of DED [21]. Given the demonstrated ability of greenness to mitigate heat and its influence on ocular conditions, it is plausible that greenness could also modify the association between temperature and DED.

As climate change progresses and DED prevalence rises, its broader public health impact becomes increasingly concerning. This study examines the association between ambient temperature and DED using a time-stratified case-crossover design, based on outpatient visit data in Beijing from 2012 to 2022. We further assess whether residential greenness, measured by the normalized difference vegetation index (NDVI), modifies the temperature-DED relationship. Our findings aim to inform strategies for reducing temperature-related DED burden and to provide valuable insights for integrating health considerations into urban planning.

Methods

Study population

This retrospective study employed a case-crossover design to examine the relationship between ambient temperature and DED. We analyzed individual-level data on outpatient visits from January 1, 2012, to October 31, 2022, at the Department of Ophthalmology of Peking University People’s Hospital in Beijing. Outpatient information, including age, gender, visit date, and residential address, was collected. Eligible participants were Beijing residents, aged 18 years or older, and diagnosed with DED, identified using International Classification of Diseases, 10th Revision (ICD-10) codes H11.103 and E50.702. Only first-visit patients were included in the analysis. We excluded patients with missing residential address information or incomplete temperature exposure data and patients with Sjogren’s syndrome, graft-versus-host disease, systemic lupus erythematosus, sarcoidosis, and rheumatoid arthritis. This study ultimately included 71,069 patients out of 171,547 DED outpatient visits from January 1, 2012, to October 31, 2022 (Fig. 1). The ethical approval was obtained from the Ethics Committee of Peking University People’s Hospital (Approved No. 2022PHB311-001).

Fig. 1 — Flow diagrams of participants selection

Study design

We investigated the association between ambient temperature and the onset of DED using the individual-level, time-stratified case-crossover design, which has been widely used in investigating the associations between environmental exposures and health events [22]. This design features that each case serves as its own control and assesses exposure variations between case and control periods, thus avoiding the impact of potential confounders that remain constant between case and control periods, such as demographic and socioeconomic factors. For each DED patient, the case period was defined as the day of the outpatient visit, and control periods (3 or 4 per case) were identified using the time-stratified approach by matching on the same day of the week within the same calendar month and year.

Environmental data

Temperature data were obtained from the ERA5-Land reanalysis dataset (https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview), which provides gridded hourly temperature estimates from 1950 to the present at a spatial resolution of 0.1° × 0.1°. In this study, hourly temperature data were aggregated to daily averages for analysis. ERA5-Land integrates high-resolution numerical simulations from the ECMWF land surface model, driven by downscaled meteorological inputs from the ERA5 climate reanalysis, with elevation corrections applied to near-surface thermodynamic variables. The dataset demonstrates strong model performance, with a Pearson correlation coefficient (R) of 0.94 for land temperature [23].

The NDVI, widely used to quantify greenness exposure in urban greening studies, measures the contrast between near-infrared (strongly reflected by vegetation) and red light (absorbed by vegetation) [24, 25]. The NDVI is a dimensionless indicator, ranging from 0 to 1, where 0 represents a high degree of desertification and 1 indicates a high level of greenness. The data were obtained from the Vegetation Index (1 month - Terra/MODIS) dataset from the NASA Earth Observations (NEO) Global Archive (https://neo.gsfc.nasa.gov/view.php?datasetId=MOD_NDVI_M), providing monthly average NDVI values from 2000 to the present with a 0.1° × 0.1° resolution (approximately 10 km × 10 km). For this study, the NDVI values range from 0.06 to 0.89.

Daily fine particulate matter (PM_2.5) concentration data were obtained from the Tracking Air Pollution in China (TAP) dataset [26], which provides near-real-time, gridded daily averages of PM_2.5 concentrations from 2000 to the present. This dataset has a spatial resolution of 10 km × 10 km and integrates information from multiple sources, including ground-based observations, satellite remote sensing data, air quality models, and other ancillary data. The TAP PM_2.5 are estimated using a two-stage machine learning model coupled with the synthetic minority oversampling technique and a tree-based gap-filling method, achieving an averaged out-of-bag cross-validation R² of 0.83 [26]. Daily precipitation data came from the Integrated Surface Database (ISD) [27], a global database maintained by the National Centers for Environmental Information (NCEI). The ISD provides high-resolution, quality-controlled weather observations from over 35,000 stations worldwide from 1973 to the present. In this study, we used daily precipitation data from a meteorological station located in Beijing (CHM00054511). Ozone (O₃) concentration data were obtained from ChinaHighO₃ dataset (https://zenodo.org/records/16883507), which provides ground-level maximum daily 8-hour average (MDA8) O₃ estimates from 2000 to the present at a 1 km × 1 km spatial resolution. The dataset is generated using artificial intelligence models that integrate multiple big data sources, including ground-based observations, satellite remote sensing products, atmospheric reanalysis, and chemical transport model simulations, while accounting for the spatiotemporal heterogeneity of air pollution. ChinaHighO₃ exhibits high accuracy, with a cross-validation coefficient of determination (CV-R²) of 0.89 [28].

Environmental datasets, including temperature, NDVI, PM_2.5, and O₃, were temporally matched by date and linked to each individual in the study based on the longitude and latitude of their residential address to ensure consistency across exposure assignments.

Statistical analysis

We used conditional logistic regression to estimate the linear association between daily average temperature and DED. The model is adjusted for precipitation, PM_2.5, and statutory holidays, and is specified as:

where Inline graphic represents the likelihood of patient developing DED at time , provides the odds ratio (OR) for the association between daily temperature () and DED, covariates () comprised three factors: precipitation, PM_2.5, and statutory holidays, accounts for fixed effects, and represents random error. In this study, we reported excess risk using Inline graphic as an approximation of the effect of short-term temperature on DED.

To assess potential lagged effects of temperature on DED, we defined same-day average temperature as lag 0 and average temperature from up to 7 days prior as lag 0-n. We conducted a cumulative lag analysis (lag 0–1 through lag 0–7) to determine the primary exposure timeframe. A fixed-effect spline logit model with a natural cubic spline function (3 degrees of freedom) was applied to estimate the potential non-linear exposure-response relationship between temperature and DED. Knots were positioned at equally spaced quantiles of the temperature distribution to ensure sufficient data coverage across the range of exposure. The degree of freedom was chosen based on goodness-of-fit statistics, model convergence, and biological plausibility to achieve an optimal balance between flexibility and parsimony. We performed subgroup analyses stratified by age and gender to explore the potential heterogeneity in effect estimates across demographic factors.

To examine the modification effect of the NDVI, we stratified the study population into tertiles based on NDVI levels to explore the potential heterogeneity in linear and nonlinear effects of temperature across different NDVI levels. A novel varying coefficient model was used to explore the potential non-linear modifying effects on the temperature-DED association [29]. The temperature effect coefficient was modeled as a smooth function of NDVI using a cubic regression spline (k = 3), allowing the temperature effect to vary continuously along the NDVI gradient. The spline basis functions were generated using generalized additive modeling and incorporated into a conditional logistic regression framework. The model is adjusted for precipitation, PM_2.5, and statutory holidays, and is specified as:

where Inline graphic denotes heterogeneous nonlinear associations between temperature and DED varying by NDVI (), covariates () comprised three factors: precipitation, PM_2.5, and statutory holidays, accounts for individual-level fixed effects, and represents random error. Stratification analyses by age and gender were performed.

For sensitivity analyses, we first conducted a time-series analysis using a quasi-Poisson regression model to estimate temperature-DED associations and thus test the robustness of the case-crossover analysis. We then applied a natural cubic spline function with 4 degrees of freedom to assess the robustness of the non-linear association between temperature and DED. Additionally, we examined the sensitivity of our results by incorporating daily O₃ concentrations as a covariate into the conditional logistic regression model.

All statistical analyses were performed using the R software (version 4.4.0; The R Foundation for Statistical Computing, Vienna, Austria). The R package survival was used to develop the conditional logistic regression model, and package mgcv was used for the varying-coefficient model.

Results

Population and environmental exposure characteristics

The mean age of the 71,069 eligible patients with DED is 54.8 ± 17.5 years, and 48,394 (68.1%) are female. The average daily temperature for case periods is 13.16 °C (standard deviation [SD] = 11.20 °C), which is slightly higher than the weighted average daily temperature for control periods (weighted mean = 13.15 °C, SD = 11.23 °C). The monthly average NDVI is 0.33 (SD = 0.14) for the studied patients (Table 1).

Table 1.

Descriptive statistics of subjects and environmental exposures during case and control periods

Variables	Participants group, No. (%)
Variables	Case periods (n = 71,069)	Control periods* (n = 71,069)
Age (years)	54.8 ± 17.5
Age group, years
18–40	16,913 (23.8)
40–60	23,444 (33.0)
≥ 60	30,712 (43.2)
Gender
Male	22,675 (31.9)
Female	48,394 (68.1)
Statutory Holidays
Yes	7254 (10.2)	11,542 (16.2)
No	63,815 (89.8)	59,527 (83.8)
Temperature (°C)	13.16 ± 11.20	13.15 ± 11.23
PM_2.5 (µg/m³)	62.39 ± 49.42	62.83 ± 49.84
Precipitation (mm/day)	1.52 ± 7.08	1.72 ± 8.34
NDVI	0.33 ± 0.14
NDVI tertile
T1 (0 ~ 0.25)	23,732 (33.4)
T2 (0.25 ~ 0.38)	23,391 (32.9)
T3 (0.38 ~ 1)	23,946 (33.7)

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*Total number, holiday distributions, and environmental exposures for control periods are weighted using 1/n weights, where n is the number of control periods per case

Linear and nonlinear associations between temperature and DED

We found a significant positive association between temperature and DED (Fig. 2). According to the fully-adjusted model, per 1 °C increase in daily average temperature was associated with a 0.8% (95% confidence interval [CI] 0.4%, 1.2%) increased risk of DED. This effect lagged for up to two days post exposure. Excess risks are 0.6% (95% CI 0.2%, 1.1%) for a cumulative lag of 1 day and 0.5% (95% CI 0.04%, 0.92%) for a cumulative lag of 2 days (Fig. 2b). Age-stratified analysis showed that younger individuals (< 40 years) were more susceptible to temperature-related DED with a 1.7% excess risk (95% CI 0.9%, 2.6%) for per 1 °C increase in daily temperature, compared to individuals aged between 40 and 60 years with a 1.0% excess risk (95% CI 0.3%, 1.7%). No significant effect was observed for the older group (≥ 60 years). The estimated association between ambient temperature and DED is generally robust to different model settings and subpopulations by gender (Fig. 2a and c).

We also explored the non-linear relationship between temperature and DED. The exposure-response curve linking DED to temperature was observed to be sublinear. As the daily average temperature increases, the risk of DED consistently rises, with the increase becoming more gradual at higher temperatures (Fig. 3).

Fig. 3 — Exposure-response function for temperature-related DED excess risk. The solid black lines are the estimates for the excess risk of DED, and the gray areas are their 95% confidence intervals.

Effect modification of greenness exposure

The effect modification analysis suggests that residential greenness has a protective effect against temperature-related DED risks. The effect of temperature on DED varies across NDVI tertiles. The strongest effect was observed in areas with the lowest residential greenness, with an excess risk of 1.8% (95% CI 1.1%, 2.5%) for the first tertile (T1). No significant effect was observed in areas with higher residential greenness (T2-T3) (Fig. 4a). The exposure-response curve for temperature-related DED excess risk estimates stratified by NDVI tertiles also shows a similar pattern (Fig. 4b). This trend is consistent across different subpopulations (Figure S1).

Fig. 4 — Modifying effects of greenness exposure on the association between temperature and DED. Panel (a) shows the linear modifying effect stratified by NDVI tertiles. Error bars indicate effect estimates with 95% confidence intervals, with corresponding numerical values annotated alongside each estimate. Panel (b) shows exposure-response function for temperature-related DED excess risk stratified by NDVI tertiles. Panel (c) shows the nonlinear modifying effect of NDVI on temperature-related DED excess risk. The solid lines are the mean estimates for the excess risk of DED, and the areas in the same color are their 95% confidence intervals.

Non-linear effect modification analysis revealed a sublinear relationship between NDVI and temperature-related DED excess risk. As NDVI increases, the excess risk of temperature-related DED decreases, with the protective effect plateauing at a threshold of 0.42. Beyond this threshold, further increases in NDVI do not yield significant additional reductions in DED excess risk, suggesting a saturation effect of greenness exposure (Fig. 4c). The non-linear relationship between NDVI and temperature-related DED excess risk is generally robust to different subpopulations (Figure S2).

Sensitivity analysis

The sensitivity analysis using a time-series quasi-Poisson regression model confirmed a consistent positive association between daily DED outpatient visits and daily average temperature, with an excess risk of 0.5% (95% CI 0.1%, 1.0%) per 1 °C temperature rise. In the time series analysis, none of the lagged temperatures (lag 0–1 to lag 0–7) were significantly associated with outpatient visits (Figure S3). To further assess model robustness, we applied a natural cubic spline function with 4 degrees of freedom, and the results were generally robust to different model settings (Figure S4). Moreover, when daily O₃ concentrations were included as a covariate in the conditional logistic regression model, per 1 °C increase in daily average temperature was associated with a 0.5% (95% CI 0.1%, 1.0%) increased risk of DED. This finding was generally consistent with the primary model excluding O₃ (excess risk: 0.8%; 95% CI: 0.4%, 1.2%), demonstrating that the temperature-DED association remained robust after O₃ adjustment.

Discussion

To our best knowledge, this is the first study to examine how residing greenness modifies the short-term effect of temperature on DED using an innovative varying-coefficient model. While previous studies have documented the effects of temperature and air pollution on DED, none have explicitly explored the interactive influence of environmental greenness, a key urban exposure that may buffer heat-related health risks. Drawing on a large-scale population in northern China, we add to existing evidence with a significant positive association between temperature and DED and provide new evidence that higher levels of residing greenness attenuate this relationship. Moreover, by quantifying the non-linear pattern of this modification, our findings advance current understanding of how microenvironmental features shape ocular surface vulnerability under climate change, providing an evidence base for integrating green infrastructure into public health and urban planning strategies.

Our study found a slightly lower temperature-related excess risk of DED compared to previous research. A case-crossover study in Taiwan, a low-latitude island in East Asia, identified DED subjects from the National Health Insurance of Taiwan (2004–2013), and reported a 1.1% (95% CI 0.5%, 1.6%) increase in DED risk per 1 °C temperature rise [6]. In the mid-latitude region, a cross-sectional study in South Korea [9], based on the Korea National Health and Nutrition Examination Survey (2010–2012), found an excess risk of 7.6% (95% CI 0.9%, 14.8%) per 1-SD temperature rise. Recent research in a cold region of northeastern China (Changchun) further supports these findings. Using a six-year crossover design (2015–2021), the study reported positive associations between DED and air temperature [30]. Beyond East Asia, evidence from a large-scale retrospective analysis of over 3.4 million patients attending Veterans Affairs eye clinics across the continental United States (2006–2011) also supports a temperature-DED link, showing a 0.8% (incidence rate ratio = 1.008; 95% CI: 1.007–1.010) increase in dry eye risk per 1-SD temperature rise [31]. A plausible mechanism for the positive association between ambient temperature and DED is increased tear evaporation at higher temperatures [32, 33]. Key factors contributing to DED pathophysiology include tear film instability, tear hyperosmolarity, ocular surface inflammation and damage, and neurosensory abnormalities [34]. Ambient temperature plays an important role in influencing the tear film’s stability and evaporation dynamics. Higher temperatures can accelerate tear evaporation, leading to tear film instability and ocular surface stress. Experimental studies have shown that the tear evaporation rate can increase more than threefold as ambient temperature rises from 5 °C to 25 °C [33]. This accelerated evaporation promotes tear film disruption, hyperosmolarity, and subsequent inflammatory responses, all of which contribute to the development and exacerbation of DED.

However, our findings on cold temperatures diverged from previous work. We observed a lower risk at lower temperatures, contrary to some studies that suggest cold weather worsens DED symptoms [35, 36]. Recent time-stratified case-crossover studies conducted in Hangzhou, China, reported that low temperatures enhanced the adverse effects of air pollution on DED [37, 38], indicating that cold environments may amplify pollution-related ocular surface stress and increase susceptibility. This discrepancy might be attributed to differences in outcome measures, specific study populations, and climate characteristics, underscoring the importance of considering local context when studying environmental health effects. Evidence linking temperature and DED in China is limited, primarily from southern regions. Our study contributes valuable insights into the effects in northern China with different climate conditions.

A key finding of our study is the protective effect of greenness, a modifiable environmental factor, against temperature-related DED. These findings expand on previous research that has shown the benefits of greenness in mitigating high-temperature-related mortality [12–14]. Research using data from the China Longitudinal Healthy Longevity Survey found that individuals residing in the lowest quartile of greenness had a 38% higher risk of mortality on extreme hot days than those residing in the highest quartile of greenness [15]. In the present study, the stronger association between temperature and DED was observed in areas with lower greenness, while this effect was attenuated or absent in areas with a higher NDVI level. The potential mechanisms underlying the protective effect of greenness on temperature-related DED include micro-climate regulation and temperature reduction through evapotranspiration [17, 39]. Other factors, such as air filtration [10] and potential influences on outdoor activity patterns [40, 41], may also contribute. The saturation effect observed at higher NDVI levels indicates that there may be a threshold beyond which additional greenness provides diminishing returns in terms of DED protection.

Our age-stratified association analyses revealed a stronger association between temperature and DED among younger individuals. This finding may be attributed to differences in physical activity patterns and environmental exposure across age groups in urban settings like Beijing. Younger adults, often leading fast-paced lives in this bustling metropolis, may have limited outdoor time and fewer interactions with green spaces, potentially increasing their vulnerability to temperature extremes. Conversely, increased leisure time post-retirement and walking as the primary leisure-time physical activity in Chinese older people [42] may allow older individuals for more interactions with residential greenness, enhancing its protective effects against temperature-related DED. These findings underscore the critical need to integrate greening strategies into urban planning policies, optimizing human-nature interactions to effectively mitigate adverse environmental health effects.

Certain limitations of this study should be acknowledged. Firstly, as a single-center study, the generalizability of our findings is limited. Extrapolating results to regions with significantly different climates and populations necessitates cautious interpretation. Further research is needed to supplement and validate these findings in diverse settings. Furthermore, there may be selection bias due to the exclusion of individuals without complete address information necessary for matching temperature and NDVI data. The imprecise geolocation data, with some entries containing only city and district information, may have introduced bias in the assessment of individual environmental exposures. Additionally, the use of outpatient visit data may not fully capture the exact onset timing of DED. The interval between symptom onset and the decision to seek medical care can vary among individuals depending on symptom awareness, perceived severity, and willingness to seek treatment. This unmeasured variability in healthcare-seeking behavior may introduce modest temporal bias. Fourth, the absence of data on time spent outdoors could have led to an incomplete understanding of the true relationship between ambient temperature and health outcomes, as the duration of exposure is a critical factor in determining the physiological impact of environmental conditions. Fifth, we acknowledge that the use of environmental exposure data at a 0.1° × 0.1° (approximately 10 km × 10 km) spatial resolution may not fully capture local variations in greenness, particularly in densely populated urban areas where environmental heterogeneity can occur at much finer spatial scales. Future studies incorporating higher-resolution datasets are warranted to better characterize small-scale exposure differences and to more accurately assess their health impacts. Our study was unable to differentiate between specific types of vegetation or assess the quality and accessibility of green spaces, factors that could potentially modify the temperature-DED association in varying ways. We suggest that further studies should examine potential modifying effects of different vegetation types and accessibility of green spaces. In addition, the absence of relative humidity data may have led to residual confounding, as humidity can influence both the physiological response to temperature and the risk of ocular surface disorders. These efforts would further enhance our understanding of how modifiable environmental factors can be leveraged to reduce the temperature-related DED burden and inform health-conscious urban planning strategies.

In conclusion, this large-scale study reveals that high temperatures have a short-term adverse effect on DED, while residential greenness may provide a protective buffer against temperature-related risks. These findings highlight the need for urban planning and public health strategies that incorporate green spaces to help mitigate the growing burden of DED in the context of climate change.

Supplementary Information

Supplementary Material 1^{(312KB, docx)}

Acknowledgements

None.

Abbreviations

DED: Dry eye disease
NDVI: Normalized difference vegetation index
CI: Confidence interval
ICD-10: International Classification of Diseases, 10th Revision
PM_2.5: Fine particulate matter
TAP: Tracking Air Pollution in China dataset
NASA: National Aeronautics and Space Administration
ISD: Integrated Surface Database
NCEI: National Centers for Environmental Information
OR: Odds ratio
SD: Standard deviation

Authors’ contributions

SL: Formal analysis, Writing original draft, Writing – review & editing. CG: Formal analysis, Validation, Writing – review & editing. JW: Conceptualization, Supervision, Writing – review & editing. PL: Supervision, Funding acquisition, Writing – review & editing.

Funding

This work was supported by the National Natural Science Foundation of China (72474010) and National Key R&D Program of China (2022YFF1203001).

Data availability

The datasets generated and analyzed during the current study are not publicly available due to privacy and ethical restrictions but are available from the corresponding author Pengfei Li on reasonable request.

Declarations

Ethics approval and consent to participate

Informed consent to participate was waived by the Ethics Committee of Peking University People’s Hospital due to the use of de-identified data. This study was conducted in compliance with the Helsinki Declaration. The ethical approval was obtained from the Ethics Committee of Peking University People’s Hospital (Approved No. 2022PHB311-001).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Shuting Liang and Chenghua Guo contributed equally to this study.

Contributor Information

Jingyi Wu, Email: joywu@pku.edu.cn.

Pengfei Li, Email: pfli@bjmu.edu.cn.

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19.Peng BA, Naduvilath TJ, Flitcroft DI, Jong M. Is myopia prevalence related to outdoor green space?. Ophthalmic Physiol Opt. 2021;41(6):1371–81. [DOI] [PubMed] [Google Scholar]
20.Zhang C, Wang C, Guo X, Xu H, Qin Z, Tao L. Effects of greenness on myopia risk and School-Level myopia prevalence among high School-Aged adolescents: Cross-sectional study. JMIR Public Health Surveill. 2023;9:e42694. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Weijing Cheng H, Wu Z, Wang L, Liang. Association between long-term green space exposure and dry eye in China. Asia-Pacific J Ophthalmol. 2025;14(3):100165. [DOI] [PubMed] [Google Scholar]
22.Carracedo-Martínez E, Taracido M, Tobias A, Saez M, Figueiras A. Case-crossover analysis of air pollution health effects: a systematic review of methodology and application. Environ Health Perspect. 2010;118(8):1173–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Joaquín Muñoz Sabater E, Dutra A, Agusti-Panareda C, Albergel G, Arduini G, Balsamo S, Boussetta M, Choulga S, Harrigan. Hans Hersbach, Brecht Martens, Diego Miralles, Maria Piles, Nemesio Rodriguez-Fernandez, Ervin Zsótér, Carlo Buontempo, J. N. Thépaut. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. 2021.
24.Twohig-Bennett C, Jones A. The health benefits of the great outdoors: A systematic review and meta-analysis of greenspace exposure and health outcomes. Environ Res. 2018;166:628–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Cao M, Guan T, Tong M, Li J, Lu H, Yang X, Wang R, Liu H, Chao B, Liu Y, Xue T. Greenspace exposure and poststroke disability: A nationwide longitudinal study. Ecotoxicol Environ Saf. 2022;246:114195. [DOI] [PubMed] [Google Scholar]
26.Geng G, Xiao Q, Liu S, Liu X, Cheng J, Zheng Y, Xue T, Tong D, Zheng B, Peng Y, Huang X, He K. Zhang. Tracking air pollution in china: near Real-Time PM(2.5) retrievals from multisource data fusion. Environ Sci Technol. 2021;55(17):12106–15. [DOI] [PubMed] [Google Scholar]
27.Smith A, Lott N. The integrated surface database: recent developments and partnerships. Bull Am Meteorol Soc. 2011;92(6):704–8. [Google Scholar]
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29.Li P, Wu J, Li J, Tong M, Liu Y, Xue T, Guan T. Association between electrocardiographic abnormalities and flood exposure among middle-aged and elderly people: A National longitudinal study in China. Environ Int. 2024;185:108484. [DOI] [PubMed] [Google Scholar]
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32.Borchman D, Foulks GN, Yappert MC, Mathews J, Leake K. Bell. Factors affecting evaporation rates of tear film components measured in vitro. Eye Contact Lens. 2009;35(1):32–7. [DOI] [PubMed] [Google Scholar]
33.Abusharha EI, Pearce R, Fagehi. Effect of ambient temperature on the human tear film. Eye Contact Lens. 2016;42(5):308–12. [DOI] [PubMed] [Google Scholar]
34.Jennifer P, Craig, Kelly K, Nichols EK, Akpek B, Caffery HS, Dua C-K, Joo Z, Liu JD, Nelson JJ, Nichols. Kazuo Tsubota, Fiona Stapleton. TFOS DEWS II definition and classification report. Ocul Surf. 2017;15(3):276–83. [DOI] [PubMed] [Google Scholar]
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36.Ho WT, Chiu CY, Chang SW. Low ambient temperature correlates with the severity of dry eye symptoms. Taiwan J Ophthalmol. 2022;12(2):191–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Mo Z, Fu Q, Lyu D, Zhang L, Qin Z, Tang Q, Yin H, Xu P, Wu L, Wang X, Lou X, Chen Z. Yao. Impacts of air pollution on dry eye disease among residents in Hangzhou, china: A case-crossover study. Environ Pollut. 2019;246:183–9. [DOI] [PubMed] [Google Scholar]
38.Chu D, Chen J, Yang C, Li Y, Wang M, Bao J. Associations between air pollution and daily outpatient visits for dry eye disease and the effect modification of temperature. BMC Public Health. 2025;25(1):1163. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Li Y, Li ZL, Wu H, Zhou C, Liu X, Leng P, Yang P, Wu W, Tang R, Shang GF, Ma L. Biophysical impacts of Earth greening can substantially mitigate regional land surface temperature warming. Nat Commun. 2023;14(1):121. [DOI] [PMC free article] [PubMed] [Google Scholar]
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41.McMorris O, Villeneuve PJ, Su J. Jerrett. Urban greenness and physical activity in a National survey of Canadians. Environ Res. 2015;137:94–100. [DOI] [PubMed] [Google Scholar]
42.Li X, Peng A, Li L, Chen L. Association between walking and square dancing-oriented leisure-time physical activity and cognitive function among middle-aged and elderly people in Southwest China. BMC Geriatr. 2023;23(1):28. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1^{(312KB, docx)}

Data Availability Statement

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[CR16] 16.Farshid Aram EH, García E, Solgi. Soran Mansournia. Urban green space cooling effect in cities. Heliyon. 2019;5(4):e01339. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR17] 17.Guo-yu Q, Li Hong-yong, Zhang Qing-tao, Chen W, Liang Xiao-jian. Xiang-ze Li. Effects of evapotranspiration on mitigation of urban temperature by vegetation and urban agriculture. J Integr Agric. 2013;12(8):1307–15. [Google Scholar]

[CR18] 18.Li L, Cheng W, Wang W, Dong G, Liang L. U-shaped association between residential greenness and keratoconus. Environ Res. 2025;267:120682. [DOI] [PubMed] [Google Scholar]

[CR19] 19.Peng BA, Naduvilath TJ, Flitcroft DI, Jong M. Is myopia prevalence related to outdoor green space?. Ophthalmic Physiol Opt. 2021;41(6):1371–81. [DOI] [PubMed] [Google Scholar]

[CR20] 20.Zhang C, Wang C, Guo X, Xu H, Qin Z, Tao L. Effects of greenness on myopia risk and School-Level myopia prevalence among high School-Aged adolescents: Cross-sectional study. JMIR Public Health Surveill. 2023;9:e42694. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR21] 21.Weijing Cheng H, Wu Z, Wang L, Liang. Association between long-term green space exposure and dry eye in China. Asia-Pacific J Ophthalmol. 2025;14(3):100165. [DOI] [PubMed] [Google Scholar]

[CR22] 22.Carracedo-Martínez E, Taracido M, Tobias A, Saez M, Figueiras A. Case-crossover analysis of air pollution health effects: a systematic review of methodology and application. Environ Health Perspect. 2010;118(8):1173–82. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR23] 23.Joaquín Muñoz Sabater E, Dutra A, Agusti-Panareda C, Albergel G, Arduini G, Balsamo S, Boussetta M, Choulga S, Harrigan. Hans Hersbach, Brecht Martens, Diego Miralles, Maria Piles, Nemesio Rodriguez-Fernandez, Ervin Zsótér, Carlo Buontempo, J. N. Thépaut. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. 2021.

[CR24] 24.Twohig-Bennett C, Jones A. The health benefits of the great outdoors: A systematic review and meta-analysis of greenspace exposure and health outcomes. Environ Res. 2018;166:628–37. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR25] 25.Cao M, Guan T, Tong M, Li J, Lu H, Yang X, Wang R, Liu H, Chao B, Liu Y, Xue T. Greenspace exposure and poststroke disability: A nationwide longitudinal study. Ecotoxicol Environ Saf. 2022;246:114195. [DOI] [PubMed] [Google Scholar]

[CR26] 26.Geng G, Xiao Q, Liu S, Liu X, Cheng J, Zheng Y, Xue T, Tong D, Zheng B, Peng Y, Huang X, He K. Zhang. Tracking air pollution in china: near Real-Time PM(2.5) retrievals from multisource data fusion. Environ Sci Technol. 2021;55(17):12106–15. [DOI] [PubMed] [Google Scholar]

[CR27] 27.Smith A, Lott N. The integrated surface database: recent developments and partnerships. Bull Am Meteorol Soc. 2011;92(6):704–8. [Google Scholar]

[CR28] 28.Jing Wei Z, Li K, Li RR, Dickerson, Rachel T, Pinker J, Wang X, Liu L, Xue SW, Cribb M. Full-coverage mapping and Spatiotemporal variations of ground-level Ozone (O3) pollution from 2013 to 2020 across China. Remote Sens Environ. 2022;270:112775. [Google Scholar]

[CR29] 29.Li P, Wu J, Li J, Tong M, Liu Y, Xue T, Guan T. Association between electrocardiographic abnormalities and flood exposure among middle-aged and elderly people: A National longitudinal study in China. Environ Int. 2024;185:108484. [DOI] [PubMed] [Google Scholar]

[CR30] 30.Lu CW, Fu J, Liu XF, Cui ZH, Chen WW, Guo L, Li XL, Ren Y, Shao F, Chen LN. Hao. Impacts of air pollution and meteorological conditions on dry eye disease among residents in a Northeastern Chinese metropolis: a six-year crossover study in a cold region. Light Sci Appl. 2023;12(1):186. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR31] 31.Anat Galor N, Kumar W, Feuer DJ. Lee. Environmental factors affect the risk of dry eye syndrome in a united States veteran populatio. Ophthalmology. 2014;121(4):972–e973971. [DOI] [PubMed] [Google Scholar]

[CR32] 32.Borchman D, Foulks GN, Yappert MC, Mathews J, Leake K. Bell. Factors affecting evaporation rates of tear film components measured in vitro. Eye Contact Lens. 2009;35(1):32–7. [DOI] [PubMed] [Google Scholar]

[CR33] 33.Abusharha EI, Pearce R, Fagehi. Effect of ambient temperature on the human tear film. Eye Contact Lens. 2016;42(5):308–12. [DOI] [PubMed] [Google Scholar]

[CR34] 34.Jennifer P, Craig, Kelly K, Nichols EK, Akpek B, Caffery HS, Dua C-K, Joo Z, Liu JD, Nelson JJ, Nichols. Kazuo Tsubota, Fiona Stapleton. TFOS DEWS II definition and classification report. Ocul Surf. 2017;15(3):276–83. [DOI] [PubMed] [Google Scholar]

[CR35] 35.Choi YH, Song MS, Lee Y, Paik HJ, Song JS, Choi YH, Kim DH. Adverse effects of meteorological factors and air pollutants on dry eye disease: a hospital-based retrospective cohort study. Sci Rep. 2024;14(1):17776. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR36] 36.Ho WT, Chiu CY, Chang SW. Low ambient temperature correlates with the severity of dry eye symptoms. Taiwan J Ophthalmol. 2022;12(2):191–7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR37] 37.Mo Z, Fu Q, Lyu D, Zhang L, Qin Z, Tang Q, Yin H, Xu P, Wu L, Wang X, Lou X, Chen Z. Yao. Impacts of air pollution on dry eye disease among residents in Hangzhou, china: A case-crossover study. Environ Pollut. 2019;246:183–9. [DOI] [PubMed] [Google Scholar]

[CR38] 38.Chu D, Chen J, Yang C, Li Y, Wang M, Bao J. Associations between air pollution and daily outpatient visits for dry eye disease and the effect modification of temperature. BMC Public Health. 2025;25(1):1163. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR39] 39.Li Y, Li ZL, Wu H, Zhou C, Liu X, Leng P, Yang P, Wu W, Tang R, Shang GF, Ma L. Biophysical impacts of Earth greening can substantially mitigate regional land surface temperature warming. Nat Commun. 2023;14(1):121. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR40] 40.Xia QF, Qin GY, Liu Q, Hu YZ. Green space exposure and Chinese residents’ physical activity participation: empirical evidence from a health geography perspective. Front Public Health. 2024;12:1430706. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR41] 41.McMorris O, Villeneuve PJ, Su J. Jerrett. Urban greenness and physical activity in a National survey of Canadians. Environ Res. 2015;137:94–100. [DOI] [PubMed] [Google Scholar]

[CR42] 42.Li X, Peng A, Li L, Chen L. Association between walking and square dancing-oriented leisure-time physical activity and cognitive function among middle-aged and elderly people in Southwest China. BMC Geriatr. 2023;23(1):28. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Association between dry eye disease and temperature is modified by residential greenness: case-crossover study in Beijing, China

Shuting Liang

Chenghua Guo

Jingyi Wu

Pengfei Li

Abstract

Background

Methods

Results

Conclusion

Supplementary Information

Introduction

Methods

Study population

Fig. 1.

Study design

Environmental data

Statistical analysis

Results

Population and environmental exposure characteristics

Table 1.

Linear and nonlinear associations between temperature and DED

Fig. 2.

Fig. 3.

Effect modification of greenness exposure

Fig. 4.

Sensitivity analysis

Discussion

Supplementary Information

Acknowledgements

Abbreviations

Authors’ contributions

Funding

Data availability

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Footnotes

Contributor Information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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